Search Results

You are looking at 81 - 90 of 116 items for

  • Author or Editor: H. Zhang x
  • Refine by Access: All Content x
Clear All Modify Search
Xiangyu Ao
,
C. S. B. Grimmond
,
H. C. Ward
,
A. M. Gabey
,
Jianguo Tan
,
Xiu-Qun Yang
,
Dongwei Liu
,
Xing Zhi
,
Hongya Liu
, and
Ning Zhang

Abstract

The Surface Urban Energy and Water Balance Scheme (SUEWS) is used to investigate the impact of anthropogenic heat flux Q F and irrigation on surface energy balance partitioning in a central business district of Shanghai. Diurnal profiles of Q F are carefully derived based on city-specific hourly electricity consumption data, hourly traffic data, and dynamic population density. The Q F is estimated to be largest in summer (mean daily peak 236 W m−2). When Q F is omitted, the SUEWS sensible heat flux Q H reproduces the observed diurnal pattern generally well, but the magnitude is underestimated compared to observations for all seasons. When Q F is included, the Q H estimates are improved in spring, summer, and autumn but are poorer in winter, indicating winter Q F is overestimated. Inclusion of Q F has little influence on the simulated latent heat flux Q E but improves the storage heat flux estimates except in winter. Irrigation, both amount and frequency, has a large impact on Q E . When irrigation is not considered, the simulated Q E is underestimated for all seasons. The mean summer daytime Q E is largely overestimated compared to observations under continuous irrigation conditions. Model results are improved when irrigation occurs with a 3-day frequency, especially in summer. Results are consistent with observed monthly outdoor water use. This study highlights the importance of appropriately including Q F and irrigation in urban land surface models—terms not generally considered in many previous studies.

Full access
A. Bodas-Salcedo
,
M. J. Webb
,
S. Bony
,
H. Chepfer
,
J.-L. Dufresne
,
S. A. Klein
,
Y. Zhang
,
R. Marchand
,
J. M. Haynes
,
R. Pincus
, and
V. O. John

Errors in the simulation of clouds in general circulation models (GCMs) remain a long-standing issue in climate projections, as discussed in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. This highlights the need for developing new analysis techniques to improve our knowledge of the physical processes at the root of these errors. The Cloud Feedback Model Intercomparison Project (CFMIP) pursues this objective, and under that framework the CFMIP Observation Simulator Package (COSP) has been developed. COSP is a flexible software tool that enables the simulation of several satellite-borne active and passive sensor observations from model variables. The flexibility of COSP and a common interface for all sensors facilitates its use in any type of numerical model, from high-resolution cloud-resolving models to the coarser-resolution GCMs assessed by the IPCC, and the scales in between used in weather forecast and regional models. The diversity of model parameterization techniques makes the comparison between model and observations difficult, as some parameterized variables (e.g., cloud fraction) do not have the same meaning in all models. The approach followed in COSP permits models to be evaluated against observations and compared against each other in a more consistent manner. This permits a more detailed diagnosis of the physical processes that govern the behavior of clouds and precipitation in numerical models. The World Climate Research Programme (WCRP) Working Group on Coupled Modelling has recommended the use of COSP in a subset of climate experiments that will be assessed by the next IPCC report. In this article we describe COSP, present some results from its application to numerical models, and discuss future work that will expand its capabilities.

Full access
Boyin Huang
,
Peter W. Thorne
,
Viva F. Banzon
,
Tim Boyer
,
Gennady Chepurin
,
Jay H. Lawrimore
,
Matthew J. Menne
,
Thomas M. Smith
,
Russell S. Vose
, and
Huai-Min Zhang

Abstract

The monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.1°–0.2°C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of SST changes over 1930s–40s when observation instruments changed rapidly. Both long- (1900–2015) and short-term (2000–15) SST trends in ERSSTv5 remain significant as in ERSSTv4.

Full access
Sergey Sokolovskiy
,
Zhen Zeng
,
Douglas C. Hunt
,
Jan-Peter Weiss
,
John J. Braun
,
William S. Schreiner
,
Richard A. Anthes
,
Ying-Hwa Kuo
,
Hailing Zhang
,
Donald H. Lenschow
, and
Teresa Vanhove

Abstract

Superrefraction at the top of the atmospheric boundary layer introduces problems for assimilation of radio occultation data in weather models. A method of detection of superrefraction by spectral analysis of deep radio occultation signals introduced earlier has been tested using 2 years of COSMIC-2/FORMOSAT-7 radio occultation data. Our analysis shows a significant dependence of the probability of detection of superrefraction on the signal-to-noise ratio, which results in a certain sampling nonuniformity. Despite this nonuniformity, the results are consistent with the known global distribution of superrefraction (mainly over the subtropical oceans) and show some additional features and seasonal variations. Comparisons to the European Centre for Medium-Range Weather Forecasts analyses and limited set of radiosondes show reasonable agreement. Being an independent measurement, detection of superrefraction from deep radio occultation signals is complementary to its prediction by atmospheric models and thus should be useful for assimilation of radio occultation data in the atmospheric boundary layer.

Open access
Yali Luo
,
Renhe Zhang
,
Qilin Wan
,
Bin Wang
,
Wai Kin Wong
,
Zhiqun Hu
,
Ben Jong-Dao Jou
,
Yanluan Lin
,
Richard H. Johnson
,
Chih-Pei Chang
,
Yuejian Zhu
,
Xubin Zhang
,
Hui Wang
,
Rudi Xia
,
Juhui Ma
,
Da-Lin Zhang
,
Mei Gao
,
Yijun Zhang
,
Xi Liu
,
Yangruixue Chen
,
Huijun Huang
,
Xinghua Bao
,
Zheng Ruan
,
Zhehu Cui
,
Zhiyong Meng
,
Jiaxiang Sun
,
Mengwen Wu
,
Hongyan Wang
,
Xindong Peng
,
Weimiao Qian
,
Kun Zhao
, and
Yanjiao Xiao

Abstract

During the presummer rainy season (April–June), southern China often experiences frequent occurrences of extreme rainfall, leading to severe flooding and inundations. To expedite the efforts in improving the quantitative precipitation forecast (QPF) of the presummer rainy season rainfall, the China Meteorological Administration (CMA) initiated a nationally coordinated research project, namely, the Southern China Monsoon Rainfall Experiment (SCMREX) that was endorsed by the World Meteorological Organization (WMO) as a research and development project (RDP) of the World Weather Research Programme (WWRP). The SCMREX RDP (2013–18) consists of four major components: field campaign, database management, studies on physical mechanisms of heavy rainfall events, and convection-permitting numerical experiments including impact of data assimilation, evaluation/improvement of model physics, and ensemble prediction. The pilot field campaigns were carried out from early May to mid-June of 2013–15. This paper: i) describes the scientific objectives, pilot field campaigns, and data sharing of SCMREX; ii) provides an overview of heavy rainfall events during the SCMREX-2014 intensive observing period; and iii) presents examples of preliminary research results and explains future research opportunities.

Full access
Joseph J. Cione
,
George H. Bryan
,
Ronald Dobosy
,
Jun A. Zhang
,
Gijs de Boer
,
Altug Aksoy
,
Joshua B. Wadler
,
Evan A. Kalina
,
Brittany A. Dahl
,
Kelly Ryan
,
Jonathan Neuhaus
,
Ed Dumas
,
Frank D. Marks
,
Aaron M. Farber
,
Terry Hock
, and
Xiaomin Chen
Full access
G. A. Vecchi
,
T. Delworth
,
R. Gudgel
,
S. Kapnick
,
A. Rosati
,
A. T. Wittenberg
,
F. Zeng
,
W. Anderson
,
V. Balaji
,
K. Dixon
,
L. Jia
,
H.-S. Kim
,
L. Krishnamurthy
,
R. Msadek
,
W. F. Stern
,
S. D. Underwood
,
G. Villarini
,
X. Yang
, and
S. Zhang

Abstract

Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system; therefore, understanding and predicting TC location, intensity, and frequency is of both societal and scientific significance. Methodologies exist to predict basinwide, seasonally aggregated TC activity months, seasons, and even years in advance. It is shown that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basinwide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal time scales, and comprises high-resolution (50 km × 50 km) atmosphere and land components as well as more moderate-resolution (~100 km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux adjustment.” A suite of 12-month duration retrospective forecasts is performed over the 1981–2012 period, after initializing the climate model to observationally constrained conditions at the start of each forecast period, using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basinwide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally aggregated regional TC activity months in advance are feasible.

Full access
Peter R. Gent
,
Gokhan Danabasoglu
,
Leo J. Donner
,
Marika M. Holland
,
Elizabeth C. Hunke
,
Steve R. Jayne
,
David M. Lawrence
,
Richard B. Neale
,
Philip J. Rasch
,
Mariana Vertenstein
,
Patrick H. Worley
,
Zong-Liang Yang
, and
Minghua Zhang

Abstract

The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Niño–Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden–Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4°C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings.

Full access
Joseph J. Cione
,
George H. Bryan
,
Ronald Dobosy
,
Jun A. Zhang
,
Gijs de Boer
,
Altug Aksoy
,
Joshua B. Wadler
,
Evan A. Kalina
,
Brittany A. Dahl
,
Kelly Ryan
,
Jonathan Neuhaus
,
Ed Dumas
,
Frank D. Marks
,
Aaron M. Farber
,
Terry Hock
, and
Xiaomin Chen

Abstract

Unique data from seven flights of the Coyote small unmanned aircraft system (sUAS) were collected in Hurricanes Maria (2017) and Michael (2018). Using NOAA’s P-3 reconnaissance aircraft as a deployment vehicle, the sUAS collected high-frequency (>1 Hz) measurements in the turbulent boundary layer of hurricane eyewalls, including measurements of wind speed, wind direction, pressure, temperature, moisture, and sea surface temperature, which are valuable for advancing knowledge of hurricane structure and the process of hurricane intensification. This study presents an overview of the sUAS system and preliminary analyses that were enabled by these unique data. Among the most notable results are measurements of turbulence kinetic energy and momentum flux for the first time at low levels (<150 m) in a hurricane eyewall. At higher altitudes and lower wind speeds, where data were collected from previous flights of the NOAA P-3, the Coyote sUAS momentum flux values are encouragingly similar, thus demonstrating the ability of an sUAS to measure important turbulence properties in hurricane boundary layers. Analyses from a large-eddy simulation (LES) are used to place the Coyote measurements into context of the complicated high-wind eyewall region. Thermodynamic data are also used to evaluate the operational HWRF model, showing a cool, dry, and thermodynamically unstable bias near the surface. Preliminary data assimilation experiments also show how sUAS data can be used to improve analyses of storm structure. These results highlight the potential of sUAS operations in hurricanes and suggest opportunities for future work using these promising new observing platforms.

Free access
Boyin Huang
,
Matthew J. Menne
,
Tim Boyer
,
Eric Freeman
,
Byron E. Gleason
,
Jay H. Lawrimore
,
Chunying Liu
,
J. Jared Rennie
,
Carl J. Schreck III
,
Fengying Sun
,
Russell Vose
,
Claude N. Williams
,
Xungang Yin
, and
Huai-Min Zhang

Abstract

This analysis estimates uncertainty in the NOAA global surface temperature (GST) version 5 (NOAAGlobalTemp v5) product, which consists of sea surface temperature (SST) from the Extended Reconstructed SST version 5 (ERSSTv5) and land surface air temperature (LSAT) from the Global Historical Climatology Network monthly version 4 (GHCNm v4). Total uncertainty in SST and LSAT consists of parametric and reconstruction uncertainties. The parametric uncertainty represents the dependence of SST/LSAT reconstructions on selecting 28 (6) internal parameters of SST (LSAT), and is estimated by a 1000-member ensemble from 1854 to 2016. The reconstruction uncertainty represents the residual error of using a limited number of 140 (65) modes for SST (LSAT). Uncertainty is quantified at the global scale as well as the local grid scale. Uncertainties in SST and LSAT at the local grid scale are larger in the earlier period (1880s–1910s) and during the two world wars due to sparse observations, then decrease in the modern period (1950s–2010s) due to increased data coverage. Uncertainties in SST and LSAT at the global scale are much smaller than those at the local grid scale due to error cancellations by averaging. Uncertainties are smaller in SST than in LSAT due to smaller SST variabilities. Comparisons show that GST and its uncertainty in NOAAGlobalTemp v5 are comparable to those in other internationally recognized GST products. The differences between NOAAGlobalTemp v5 and other GST products are within their uncertainties at the 95% confidence level.

Open access